气动加载系统的粒子群-滑模观测器控制

发布时间：2018-01-28 04:53

本文关键词： 图像分割气动加载伺服控制系统粒子群滑模干扰观测器混沌系统　出处：《控制工程》2017年11期 　论文类型：期刊论文

【摘要】：针对气动加载伺服控制系统的时滞、强非线性,提出一种基于混沌粒子群(CPSO)的改进滑模干扰观测器(ISMDO)的控制方案。利用观测器预估理论对实际输出进行估计,计算出无延时的预估输出,并将此输出值与设定值的误差作为滑模控制器的输入计算控制量。同时采用粒子群算法进行控制器的参数寻优,为使寻优效果更好,首先采用混沌反学习法"初选"粒子,再利用"淘汰"条件对粒子群算法进行筛选,并基于混沌系统替换粒子策略对群体进行补充。通过与PID控制算法,滑模干扰观测器(SMDO)等不同控制策略对阶跃、正弦信号的系统仿真进行比较,证明算法能较好的解决系统的延迟和非线性。并通过试验验证对于气动加载系统来说,该算法具有较好的控制性能。
[Abstract]:Aiming at the time-delay of pneumatic loaded servo control system, it is strongly nonlinear. An improved sliding mode disturbance observer (ISMDO) based on chaotic particle swarm optimization (CPSO) is proposed. The estimated output without delay is calculated, and the error between the output value and the set value is taken as the input of the sliding mode controller to calculate the control quantity. At the same time, the particle swarm optimization algorithm is used to optimize the parameters of the controller in order to make the optimization effect better. First, the chaotic backlearning method is used to "primary" particles, then the "elimination" condition is used to screen the particle swarm optimization algorithm, and then the population is supplemented by the chaotic system replacement particle strategy. The algorithm is controlled by PID. Different control strategies such as sliding mode disturbance observer (SMDO) are used to compare the system simulation of step and sinusoidal signals. It is proved that the algorithm can solve the delay and nonlinearity of the system, and it is proved by experiments that the algorithm has better control performance for the pneumatic loading system.
【作者单位】：上海理工大学机械学院;
【基金】：上海市自然科学基金项目(13ZR458500)
【分类号】：TP273
【正文快照】： 气动加载伺服控制系统具有低成本,无污染,实用性强等优势,所以在控制领域中被广泛应用。但因气体所具有的可压缩性和流动状态复杂性,以及阀的非线性,使气动加载伺服控制系统具有延迟和很强的非线性等问题[1]。因此,气动加载伺服控制系统很难实现高精度的闭环控制,如何实现高精

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